Speaker recognition is an important application that may provide new modalities for multifactor biometric device login or authentication. Furthermore, speaker recognition may transform speech applications from generic to personalized by recognizing particular speakers from a group or the like. For example, recognizing particular speakers from a group may provide for improved speech transcription (e.g., as the transcription of the speaker may then be based on the particular characteristics of that speaker), associating particular portions of a transcription with the speakers that uttered the portions, or the like.
A variety of techniques may be employed to perform speaker recognition. For example, in the context of speaker verification, where a claimed identity may be identified or evaluated based on a spoken utterance, a final result scored based on the utterance and the application of a speaker model may be compared to a threshold, which may quantify the minimum similarity required for a positive verification of the utterance. For example, the threshold may provide a balance between false rejection and false acceptance.
It may be advantageous to provide speaker recognition with improved accuracy (e.g., lower false rejection rates and false acceptance rates). It is with respect to these and other considerations that the present improvements have been needed. Such improvements may become critical as the desire to provide high quality speaker recognition becomes more widespread.